Merge branch 'main' into main

Makefile

@@ -41,6 +41,23 @@ validate-yamls: ## Validate all YAML configuration files
 
 .PHONY: help
 help: ## Display this help message
-	@python -c "import re; \
+	@uv run python -c "import re; \
 	p=r'$(firstword $(MAKEFILE_LIST))'.strip(); \
 	[print(f'{m[0]:<20} {m[1]}') for m in re.findall(r'^([a-zA-Z_-]+):.*?## (.*)$$', open(p, encoding='utf-8').read(), re.M)]" | sort
+
+# ==============================================================================
+# Quality Checks
+# ==============================================================================
+
+.PHONY: check-backend
+check-backend: ## Run backend quality checks (tests + linting)
+	@$(MAKE) backend-tests
+	@$(MAKE) backend-lint
+
+.PHONY: backend-tests
+backend-tests: ## Run backend tests
+	@uv run pytest -v
+
+.PHONY: backend-lint
+backend-lint: ## Run backend linting
+	@uvx ruff check .

frontend/src/pages/LaunchView.vue

@@ -2390,6 +2390,7 @@ watch(
   flex-direction: column;
   pointer-events: auto;
   z-index: auto;
+  min-height: 0;
 }
 
 .chat-panel-fullscreen .chat-panel-content {
@@ -2408,6 +2409,7 @@ watch(
   flex-direction: column;
   gap: 12px;
   min-width: 0;
+  min-height: 0;
   pointer-events: auto;
   background: rgba(26, 26, 26, 0.92);
   border: 1px solid rgba(255, 255, 255, 0.1);
@@ -2463,6 +2465,7 @@ watch(
   overflow: hidden;
   display: flex;
   flex-direction: column;
+  min-height: 0;
 }
 
 .chat-messages::-webkit-scrollbar {

pyproject.toml

@@ -45,5 +45,19 @@ dependencies = [
 requires = ["hatchling"]
 build-backend = "hatchling.build"
 
+[tool.pytest.ini_options]
+pythonpath = ["."]
+testpaths = ["tests"]
+python_files = ["test_*.py"]
+python_classes = ["Test*"]
+python_functions = ["test_*"]
+addopts = "-v --tb=short"
+filterwarnings = [
+    # Upstream SWIG issue in faiss-cpu on Python 3.12; awaiting SWIG 4.4 fix.
+    "ignore:builtin type Swig.*:DeprecationWarning",
+    "ignore:builtin type swigvarlink.*:DeprecationWarning",
+]
+
+
 [tool.uv]
 package = false

runtime/node/agent/memory/embedding.py

@@ -86,6 +86,7 @@ class OpenAIEmbedding(EmbeddingBase):
         self.max_length = embedding_config.params.get('max_length', 8191)
         self.use_chunking = embedding_config.params.get('use_chunking', False)
         self.chunk_strategy = embedding_config.params.get('chunk_strategy', 'average')
+        self._fallback_dim = 1536  # Default; updated after first successful call
 
         if self.base_url:
             self.client = openai.OpenAI(api_key=self.api_key, base_url=self.base_url)
@@ -99,7 +100,7 @@ class OpenAIEmbedding(EmbeddingBase):
         
         if not processed_text:
             logger.warning("Empty text after preprocessing")
-            return [0.0] * 1536  # Return a zero vector
+            return [0.0] * self._fallback_dim
         
         # Handle long text via chunking
         if self.use_chunking and len(processed_text) > self.max_length:
@@ -115,17 +116,18 @@ class OpenAIEmbedding(EmbeddingBase):
                 encoding_format="float"
             )
             embedding = response.data[0].embedding
+            self._fallback_dim = len(embedding)
             return embedding
         except Exception as e:
             logger.error(f"Error getting embedding: {e}")
-            return [0.0] * 1536  # Return zero vector as fallback
+            return [0.0] * self._fallback_dim
 
     def _get_chunked_embedding(self, text: str) -> List[float]:
         """Chunk long text, embed each chunk, then aggregate."""
         chunks = self._chunk_text(text, self.max_length // 2)  # Halve the chunk length
         
         if not chunks:
-            return [0.0] * 1536
+            return [0.0] * self._fallback_dim
         
         chunk_embeddings = []
         for chunk in chunks:
@@ -141,7 +143,7 @@ class OpenAIEmbedding(EmbeddingBase):
                 continue
         
         if not chunk_embeddings:
-            return [0.0] * 1536
+            return [0.0] * self._fallback_dim
         
         # Aggregation strategy
         if self.chunk_strategy == 'average':
@@ -163,6 +165,7 @@ class LocalEmbedding(EmbeddingBase):
         super().__init__(embedding_config)
         self.model_path = embedding_config.params.get('model_path')
         self.device = embedding_config.params.get('device', 'cpu')
+        self._fallback_dim = 768  # Default; updated after first successful call
         
         if not self.model_path:
             raise ValueError("LocalEmbedding requires model_path parameter")
@@ -179,11 +182,13 @@ class LocalEmbedding(EmbeddingBase):
         processed_text = self._preprocess_text(text)
         
         if not processed_text:
-            return [0.0] * 768  # Return zero vector
+            return [0.0] * self._fallback_dim
         
         try:
             embedding = self.model.encode(processed_text, convert_to_tensor=False)
-            return embedding.tolist()
+            result = embedding.tolist()
+            self._fallback_dim = len(result)
+            return result
         except Exception as e:
             logger.error(f"Error getting local embedding: {e}")
-            return [0.0] * 768  # Return zero vector as fallback
+            return [0.0] * self._fallback_dim

runtime/node/agent/memory/file_memory.py

@@ -123,11 +123,19 @@ class FileMemory(MemoryBase):
         query_embedding = query_embedding.reshape(1, -1)
         faiss.normalize_L2(query_embedding)
 
+        expected_dim = query_embedding.shape[1]
+
         # Collect embeddings from memory items
         memory_embeddings = []
         valid_items = []
         for item in self.contents:
             if item.embedding is not None:
+                if len(item.embedding) != expected_dim:
+                    logger.warning(
+                        "Skipping memory item %s: embedding dim %d != expected %d",
+                        item.id, len(item.embedding), expected_dim,
+                    )
+                    continue
                 memory_embeddings.append(item.embedding)
                 valid_items.append(item)
 

runtime/node/agent/memory/simple_memory.py

@@ -1,5 +1,6 @@
 import hashlib
 import json
+import logging
 import os
 import re
 import time
@@ -16,6 +17,8 @@ from runtime.node.agent.memory.memory_base import (
 import faiss
 import numpy as np
 
+logger = logging.getLogger(__name__)
+
 class SimpleMemory(MemoryBase):
     def __init__(self, store: MemoryStoreConfig):
         config = store.as_config(SimpleMemoryConfig)
@@ -107,10 +110,18 @@ class SimpleMemory(MemoryBase):
         inputs_embedding = inputs_embedding.reshape(1, -1)
         faiss.normalize_L2(inputs_embedding)
 
+        expected_dim = inputs_embedding.shape[1]
+
         memory_embeddings = []
         valid_items = []
         for item in self.contents:
             if item.embedding is not None:
+                if len(item.embedding) != expected_dim:
+                    logger.warning(
+                        "Skipping memory item %s: embedding dim %d != expected %d",
+                        item.id, len(item.embedding), expected_dim,
+                    )
+                    continue
                 memory_embeddings.append(item.embedding)
                 valid_items.append(item)
         

tests/test_memory_embedding_consistency.py

@@ -0,0 +1,157 @@
+"""Tests for memory embedding dimension consistency."""
+from unittest.mock import MagicMock, patch
+from runtime.node.agent.memory.memory_base import MemoryContentSnapshot, MemoryItem
+from runtime.node.agent.memory.simple_memory import SimpleMemory
+
+def _make_store(memory_path=None):
+    """Build a minimal MemoryStoreConfig mock for SimpleMemory."""
+    simple_cfg = MagicMock()
+    simple_cfg.memory_path = memory_path
+    simple_cfg.embedding = None  # We'll set embedding manually
+
+    store = MagicMock()
+    store.name = "test_store"
+    store.as_config.return_value = simple_cfg
+    return store
+
+
+def _make_embedding(dim: int):
+    """Create a mock EmbeddingBase that produces vectors of the given dimension."""
+    emb = MagicMock()
+    emb.get_embedding.return_value = [0.1] * dim
+    return emb
+
+
+def _make_memory_item(item_id: str, dim: int):
+    """Create a MemoryItem with an embedding of the specified dimension."""
+    return MemoryItem(
+        id=item_id,
+        content_summary=f"content for {item_id}",
+        metadata={},
+        embedding=[float(i) for i in range(dim)],
+    )
+
+
+class TestSimpleMemoryRetrieveMixedDimensions:
+
+    def test_mixed_dimensions_does_not_crash(self):
+        """Retrieve with mixed-dimensional embeddings MUST not raise."""
+        store = _make_store()
+        memory = SimpleMemory(store)
+        memory.embedding = _make_embedding(dim=768)
+
+        # 3 items with correct dim, 2 with wrong dim
+        memory.contents = [
+            _make_memory_item("ok_1", 768),
+            _make_memory_item("bad_1", 1536),
+            _make_memory_item("ok_2", 768),
+            _make_memory_item("bad_2", 256),
+            _make_memory_item("ok_3", 768),
+        ]
+
+        query = MemoryContentSnapshot(text="test query")
+        # Should NOT raise ValueError / numpy error
+        results = memory.retrieve(
+            agent_role="tester",
+            query=query,
+            top_k=5,
+            similarity_threshold=-1.0,
+        )
+        # Only the 3 correct-dimension items should be candidates
+        assert len(results) <= 3
+
+    def test_all_same_dimension_returns_results(self):
+        """When all embeddings share the correct dimension, all are candidates."""
+        store = _make_store()
+        memory = SimpleMemory(store)
+        memory.embedding = _make_embedding(dim=768)
+
+        memory.contents = [
+            _make_memory_item("a", 768),
+            _make_memory_item("b", 768),
+        ]
+
+        query = MemoryContentSnapshot(text="test query")
+        results = memory.retrieve(
+            agent_role="tester",
+            query=query,
+            top_k=5,
+            similarity_threshold=-1.0,
+        )
+        assert len(results) == 2
+
+    def test_all_wrong_dimension_returns_empty(self):
+        """When every stored embedding has a wrong dimension, return empty."""
+        store = _make_store()
+        memory = SimpleMemory(store)
+        memory.embedding = _make_embedding(dim=768)
+
+        memory.contents = [
+            _make_memory_item("x", 1536),
+            _make_memory_item("y", 1536),
+        ]
+
+        query = MemoryContentSnapshot(text="test query")
+        results = memory.retrieve(
+            agent_role="tester",
+            query=query,
+            top_k=5,
+            similarity_threshold=-1.0,
+        )
+        assert results == []
+
+
+class TestOpenAIEmbeddingDynamicFallback:
+
+    def test_fallback_uses_model_dimension_after_success(self):
+        """After a successful call the fallback dimension MUST match the model."""
+        from runtime.node.agent.memory.embedding import OpenAIEmbedding
+
+        cfg = MagicMock()
+        cfg.base_url = "http://localhost:11434/v1"
+        cfg.api_key = "test"
+        cfg.model = "test-model"
+        cfg.params = {}
+
+        emb = OpenAIEmbedding(cfg)
+        assert emb._fallback_dim == 1536  # default before any call
+
+        # Simulate a successful 768-dim response
+        mock_data = MagicMock()
+        mock_data.embedding = [0.1] * 768
+        mock_response = MagicMock()
+        mock_response.data = [mock_data]
+
+        with patch.object(emb.client.embeddings, "create", return_value=mock_response):
+            result = emb.get_embedding("hello world")
+
+        assert len(result) == 768
+        assert emb._fallback_dim == 768  # updated after success
+
+    def test_fallback_zero_vector_matches_cached_dim(self):
+        """After caching dim, fallback zero-vectors MUST use that dim."""
+        from runtime.node.agent.memory.embedding import OpenAIEmbedding
+
+        cfg = MagicMock()
+        cfg.base_url = "http://localhost:11434/v1"
+        cfg.api_key = "test"
+        cfg.model = "test-model"
+        cfg.params = {}
+
+        emb = OpenAIEmbedding(cfg)
+
+        # Simulate successful 512-dim call
+        mock_data = MagicMock()
+        mock_data.embedding = [0.1] * 512
+        mock_response = MagicMock()
+        mock_response.data = [mock_data]
+
+        with patch.object(emb.client.embeddings, "create", return_value=mock_response):
+            emb.get_embedding("first call")
+
+        # Now simulate a failure — fallback should be 512-dim
+        with patch.object(emb.client.embeddings, "create", side_effect=Exception("API down")):
+            fallback = emb.get_embedding("failing call")
+
+        assert len(fallback) == 512
+        assert all(v == 0.0 for v in fallback)